kingers Posted April 2 Report Share Posted April 2 Mastering Data Analysis With Polars In Python: Crash Course Published 4/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 863.32 MB[/center] | Duration: 2h 49m Unlock the Power of Polars for Fast and Efficient Data Analysis in Python - Dive into Data Science Today! What you'll learn Understand the fundamentals of Polars, a high-performance data manipulation library in Python. Learn essential data processing techniques including filtering, aggregating, and transforming data. Master advanced data manipulation tasks such as joins, groupings, and window functions. Gain insights into optimizing performance and improving efficiency when working with large datasets. Develop the skills to tackle complex data analysis challenges and derive meaningful insights. Explore practical examples and real-world datasets to solidify understanding. Become proficient in leveraging Polars for fast and efficient data analysis in Python. Understand techniques for working with large CSV files efficiently using Polars. Learn strategies to optimize memory usage and processing speed when dealing with massive datasets. Gain practical experience in applying Polars to analyze and manipulate extensive CSV datasets with ease. Requirements Basic understanding of Python programming. Familiarity with data structures like lists, dictionaries, and tuples. Prior knowledge of data analysis concepts is beneficial but not required. Access to a computer with Python and Polars library installed (installation instructions will be provided). Description Welcome to "Mastering Data Analysis with Polars in Python: Crash Course"! Are you ready to take your data analysis skills to the next level? In this course, we'll explore the powerful capabilities of Polars, a high-performance data manipulation library, and discover how it can revolutionize your approach to data analysis. Get ready to dive into a hands-on learning experience that will propel you toward becoming a proficient data analyst in Python!What You Will Learn:Understand the fundamentals of Polars and its advantages over other data manipulation libraries.Learn essential data processing techniques, including filtering, aggregating, and transforming data using Polars.Master advanced data manipulation tasks such as joins, groupings, and window functions with ease.Explore practical examples and real-world datasets to solidify your understanding of Polars in action.Gain insights into optimizing performance and improving efficiency when working with large datasets.Develop the skills to tackle complex data analysis challenges and derive meaningful insights from your data.Who Is This Course For:This course is designed for Python enthusiasts, data analysts, data scientists, and anyone interested in unlocking the power of Polars for efficient data analysis. Whether you're a beginner looking to dive into data analysis or an experienced professional seeking to enhance your skills, this crash course will provide you with the knowledge and tools you need to succeed.Join us on this exciting journey as we delve into the world of data analysis with Polars in Python. By the end of this course, you'll be equipped with the expertise to tackle a wide range of data analysis tasks efficiently and effectively. Don't miss out on this opportunity to elevate your data analysis skills and become a master of Polars. Enroll now and let's embark on this transformative learning experience together! Overview Section 1: Introduction and Setting Up Your Environment Lecture 1 Installing Python and Setting Up Your Environment Lecture 2 How To create VENV Lecture 3 How to Install Python 3 and Use Virtual Environments (venv) on Windows- Article Lecture 4 How to Install Python 3 and Use Virtual Environments (venv) on linux- Article Lecture 5 How to Install Python 3 and Use Virtual Environments (venv) on Mac- Article Lecture 6 How to Install Jupyter Lab - Practicle Lecture 7 How to Install Jupyter Lab - Article Section 2: Python Programming Foundations Lecture 8 Functions in Python: Definition and Usage Lecture 9 Modules and Packages: Organizing Code Lecture 10 Understanding Python Classes and Objects Section 3: Introduction to Polars and Data Analysis Lecture 11 Getting Started with Series Polars: Basic Operations Lecture 12 Getting Started with DataFrame Polars: Basic Operations Lecture 13 Reading and Writing CSV Files with Polars Lecture 14 Reading and Writing Excel Files with Polars Lecture 15 Converting Pandas DataFrames to Polars Section 4: Basic Data Processing with Polars Lecture 16 Introduction to Filtering and Selecting Data in Polars Lecture 17 Filtering Data with Polars Lecture 18 Selecting Columns and Rows with Polars Lecture 19 Slicing and Sampling Data with Polars Lecture 20 Sorting Data with Polars Section 5: Aggregations and Grouping in Polars Lecture 21 Introduction to Aggregations and Grouping in Polars Lecture 22 Aggregating Data with Polars: min, max, mean, median, sum Lecture 23 Ranking Data with Polars Lecture 24 Grouping Data with Polars Lecture 25 Pivot Tables and Cross-Tabulations with Polars Section 6: Merging DataFrames with Polars Lecture 26 Joins and Concatenations Lecture Lecture 27 Understanding Concatenation in Polars Lecture 28 Understanding Join Types in Polars Section 7: Optimizing Performance with Polars Lecture 29 Memory Management: Handling Large Datasets with Polars Lecture 30 Parallel Processing: Speeding Up Data Analysis with Polars Section 8: Real-world Applications and Case Studies Lecture 31 Analyzing Financial Data with Polars Section 9: Conclusion Lecture 32 Conclusion And Recap Python enthusiasts eager to enhance their data analysis skills.,Data analysts seeking to expand their toolkit with Polars.,Data scientists interested in leveraging efficient data manipulation techniques.,Beginners looking to enter the field of data analysis with Python.,Professionals aiming to optimize their data processing workflows.,Individuals familiar with Pandas who want to explore alternative data manipulation libraries like Polars.,Python developers looking to transition from Pandas to Polars for faster and more efficient data analysis.https://rapidgator.net/file/09160cbb28197f5ebe8bcd6c164d4ac7/Mastering_Data_Analysis_with_Polars_in_Python_Crash.ziphttps://voltupload.com/g6hrid6fea7o/Mastering_Data_Analysis_with_Polars_in_Python_Crash.zipFree search engine download: Mastering Data Analysis with Polars in Python Crash Link to comment Share on other sites More sharing options...
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